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IMPPDS - Indian Medicinal Plants Protein Dataset
S. Anandakumar, V. Saravanan and P. Shanmughavel*
DBT Bioinformatics Centre, Department of Bioinformatics, Bharathiar University
*Corresponding author: Dr. P. Shanmughavel,
Coimbatore – 641046, TamilNadu, India
E-mail : shanvel_99@yahoo.com
Received July 02, 2008; Accepted July 16, 2008; Published July 17, 2008
Citation: Anandakumar s, Saravanan V, Shanmughavel P (2008) IMPPDS - Indian Medicinal Plants Protein Dataset. J Proteomics Bioinform 1: 230-232. doi:10.4172/jpb.1000028
Copyright: © 2008 Anandakumar S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


There is a growing concenter on the importance of medicinal plants in modern health care. The medicinal plant proteins have therapeutic, cosmetic, and other beneficial properties for mankind to treat various diseases. In this regard, 3D models of medicinal plants protein will enhance our knowledge about the identification of new drugs for various diseases and disorders. So, in this work we modeled about 181 various plant proteins of 18 different medicinal plants using molecular modeling techniques (Indian medicinal plants) and made into a dataset IMPPDS. The models are constructed using MODELLER 9v2, with careful manual pair wise alignment. The model templates are obtained based on the sequence similarity (40% identity) of known protein structure using BLASTP (Basic Local Alignment Tool for Protein). One or more potential structural templates were selected from PDB (Protein Databank). We used iterative conjugate gradient method for optimizing the model and PROCHECK for validation.

Key Words
Medicinal plant; Molecular modeling; Pairwise alignment; Dataset; Optimizing

Many of the medicinal plant proteins have therapeutic, cosmetic, or other beneficial properties (Balunas and Kinghorn, 2005). Three-dimensional structures for most of the Indian medicinal plants protein are limited (http://modbase.compbio.ucsf.edu/modbase-cgi/index.cgi, http://www.rcsb.org/pdb). Detailed study on such proteins structure will provide room for innovative applications and help the researchers to have a better insight on the functions and molecular properties of the proteins and to come out with new therapeutic values from corresponding protein (http://www.sristi.org/cms/). Also, there exists the obstacle of designing the accurate protein models based on automated computational modeling, chiefly in pair wise alignment (Kolinski and Gront, 2007). So, we performed the modeling procedure in a semi-automated manner with necessary manual pair wise alignment and error interpretation. In this article we described about the dataset of protein model containing 181 different proteins from 18 different medicinal plants.


Target Sequence Retrieval
Names of Medicinal plants protein which have medicinal properties were obtained from the SRISTI
(http://www.sristi.org/cms/) and sequences were taken from dataset of Swissprot/TrEMBL (http://www.expasy.ch/sprot/ ). All the taken protein sequences were ascertained that the three-dimensional structure of the corresponding proteins were not available in Protein Data Bank and Modbase (http://modbase.compbio.ucsf.edu/modbase-cgi/index.cgi,
http://www.rcsb.org/pdb). Totally 181 proteins sequence from 18 different medicinal plants were taken.

Template Searching
One or more suitable template proteins for each of the annotated medicinal plants protein were identified based on the sequence similarity (= 40% identity) using BLASTP (Basic Local Alignment Tool for Protein). For each candidate, templates with higher similarity were obtained from Protein Databank (http://www.rcsb.org/pdb).

Figure 1: Indian Medicinal Plants Protein Dataset, the dataset snapshot and visual models are shown

Sequence Alignment

Sequence alignment of target and template proteins were derived using the program align2d (alignment package in Modeler). Further, each alignment is manually curated for reduced gaps, insertions and deletions.

Rough Model
A rough 3-D model was constructed for each proteins from the sequence alignment between annotated protein and its corresponding template proteins using MODELLER 9v2 (Eswar et al., 2003) with default parameters of energy minimization value.

Model Refinement

Using the steepest descent and conjugate gradient technique (Sali and Blundell, 1993), the rough constructed models were solvated and subjected to constraint energy minimization. To eliminate bad contacts between protein atoms and structural water molecules the harmonic constraint was set to100 kJ/mol/Å2, applied for all protein atoms.

Evaluation of Refined Model
The refined structure of the models were subjected to a series of tests (Sali et al., 1995), which includes backbone conformation, evaluated by the inspection of the Psi/Phi Ramachandran plot obtained from PROCHECK (http://www.biochem.ucl.ac.uk/~roman/procheck/procheck.html) analysis, packing quality of the refined structure, investigated by the calculation of PROCHECK Quality Control value and the models can be viewed using PyMol (http://www.delsci.com/rel/099/) and RasMol.

Usefullness And Biological Community

This dataset contains 181 protein models [figure 1] from eighteen different various Indian Medicinal plants such as Bauhinia variegata, Camellia sinensis, Allium cepa, Allium ascalonicum, Hibiscus rosasinensis, Punica granatum, Brassica juncea, Mangifera indica, Euphorbia pulcherrima, Aloe vera, Crotalaria juncea, Coriandrum sativum, Cuscuta reflexa, CyNodon dactylon, Citrus sinensis, Citrus reticulata, Citrus clementina and Citrus limon. Each protein models were curated and verified manually. Detailed study on such modelled Indian medicinal plants protein structure will provide room for innovative applications and help the researchers to have a better insight on the functions and molecular properties of the proteins and to come out with new therapeutic values.


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